Showing papers in "IEEE Internet of Things Journal in 2015"

An IoT-Aware Architecture for Smart Healthcare Systems

Abstract: Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective

Abstract: Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors’ recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.

An Analysis of RFID Authentication Schemes for Internet of Things in Healthcare Environment Using Elliptic Curve Cryptography

Abstract: Advances in information and communication technologies have led to the emergence of Internet of Things (IoT). In the healthcare environment, the use of IoT technologies brings convenience to physicians and patients as they can be applied to various medical areas (such as constant real-time monitoring, patient information management, medical emergency management, blood information management, and health management). The radio-frequency identification (RFID) technology is one of the core technologies of IoT deployments in the healthcare environment. To satisfy the various security requirements of RFID technology in IoT, many RFID authentication schemes have been proposed in the past decade. Recently, elliptic curve cryptography (ECC)-based RFID authentication schemes have attracted a lot of attention and have been used in the healthcare environment. In this paper, we discuss the security requirements of RFID authentication schemes, and in particular, we present a review of ECC-based RFID authentication schemes in terms of performance and security. Although most of them cannot satisfy all security requirements and have satisfactory performance, we found that there are three recently proposed ECC-based authentication schemes suitable for the healthcare environment in terms of their performance and security.

An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments

Abstract: Smart energy in buildings is an important research area of Internet of Things (IoT). As important parts of the smart grids, the energy efficiency of buildings is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect 1-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be “green” by design. Inspired by “energy proportional computing” in modern computers, we propose an IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud-computing technologies to enable multiscale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments, which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.

A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications

Abstract: With the growing interest in the use of autonomous computing, sensing and actuating devices for various applications such as smart grids, home networking, smart environments and cities, health care, and machine-to-machine (M2M) communication has become an important networking paradigm. However, in order to fully exploit the applications facilitated by M2M communications, adequate support from all layers in the network stack must first be provided in order to meet their service requirements. This paper presents a survey of the requirements, technical challenges, and existing work on medium access control (MAC) layer protocols for supporting M2M communications. This paper first describes the issues related to efficient, scalable, and fair channel access for M2M communications. Then, in addition to protocols that have been developed specifically for M2M communications, this paper reviews existing MAC protocols and their applicability to M2M communications. This survey paper then discusses ongoing standardization efforts and open problems for future research in this area.

A Survey of Incentive Techniques for Mobile Crowd Sensing

Abstract: Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.

Software-Defined Networking for RSU Clouds in Support of the Internet of Vehicles

Abstract: We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations.

Friendship Selection in the Social Internet of Things: Challenges and Possible Strategies

Abstract: The Internet of Things (IoT) is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications, and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things (SIoT) has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner, with only local information. In the SIoT it is very important to set appropriate rules in the objects to select the right friends as these impact the performance of services developed on top of this social network. In this work, we addressed this issue by analyzing possible strategies for the benefit of overall network navigability. We first propose five heuristics, which are based on local network properties and that are expected to have an impact on the overall network structure. We then perform extensive experiments, which are intended to analyze the performance in terms of giant components, average degree of connections, local clustering, and average path length. Unexpectedly, we discovered that minimizing the local clustering in the network allowed for achieving the best results in terms of average path length. We have conducted further analysis to understand the potential causes, which have been found to be linked to the number of hubs in the network.

DIAT: A Scalable Distributed Architecture for IoT

Abstract: The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture—comprising smart control and actuation—has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

Abstract: The term Internet of Things (IoT) refers to the interaction and communication between billions of devices that produce and exchange data related to real-world objects (i.e. things). Extracting higher level information from the raw sensory data captured by the devices and representing this data as machine-interpretable or human-understandable information has several interesting applications. Deriving raw data into higher level information representations demands mechanisms to find, extract, and characterize meaningful abstractions from the raw data. This meaningful abstractions then have to be presented in a human and/or machine-understandable representation. However, the heterogeneity of the data originated from different sensor devices and application scenarios such as e-health, environmental monitoring, and smart home applications, and the dynamic nature of sensor data make it difficult to apply only one particular information processing technique to the underlying data. A considerable amount of methods from machine-learning, the semantic web, as well as pattern and data mining have been used to abstract from sensor observations to information representations. This paper provides a survey of the requirements and solutions and describes challenges in the area of information abstraction and presents an efficient workflow to extract meaningful information from raw sensor data based on the current state-of-the-art in this area. This paper also identifies research directions at the edge of information abstraction for sensor data. To ease the understanding of the abstraction workflow process, we introduce a software toolkit that implements the introduced techniques and motivates to apply them on various data sets.

Coalition Games for Spatio-Temporal Big Data in Internet of Vehicles Environment: A Comparative Analysis

Abstract: The evolution of Internet of Things (IoT) leads to the emergence of Internet of Vehicles (IoV). In IoV, nodes/vehicles are connected with one another to form a vehicular ad hoc network (VANET). But, due to constant topological changes, database repository (centralized/distributed) in IoV is of spatio-temporal nature, as it contains traffic-related data, which is dependent on time and location from a large number of inter-connected vehicles. The nature of collected data varies in size, volume, and dimensions with the passage of time, which requires large storage and computation time for processing. So, one of the biggest challenges in IoV is to process this large volume of data and later on deliver to its destination with the help of a set of the intermediate/relay nodes. The intermediate/relay nodes may act either in cooperative or non-cooperative mode for processing the spatio-temporal data. This paper analyze this problem using Bayesian coalition game (BCG) and learning automata (LA). The LA stationed on the vehicles are assumed as the players in the game. For each action performed by an automaton, it may get a reward or a penalty from the environment using which each automaton updates its action probability vector for all the actions to be taken in future. A detailed comparison has been provided by analyzing the cooperative and noncooperative nature of the players in the game. The existence of Nash equilibrium (NE) with respect to the probabilistic belief of the strategies of the other players in the coalition game is also analyzed.

A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications

Abstract: Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring measurements of users while protecting individual’s privacy in smart grid communications. In this paper, a new secure data aggregation scheme, named d ifferentially p rivate data a ggregation with f ault t olerance (DPAFT), is proposed, which can achieve differential privacy and fault tolerance simultaneously. Specifically, inspired by the idea of Diffie–Hellman key exchange protocol, an artful constraint relation is constructed for data aggregation. With this novel constraint, DPAFT can support fault tolerance of malfunctioning smart meters efficiently and flexibly. In addition, DPAFT is also enhanced to resist against differential attacks, which are suffered in most of the existing data aggregation schemes. By improving the basic Boneh–Goh–Nissim cryptosystem to be more applicable to the practical scenarios, DPAFT can resist much stronger adversaries, i.e., user’s privacy can be protected in the honest-but-curious model. Extensive performance evaluations are further conducted to illustrate that DPAFT outperforms the state-of-the-art data aggregation schemes in terms of storage cost, computation complexity, utility of differential privacy, robustness of fault tolerance, and the efficiency of user addition and removal.

Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

Abstract: Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.

Lightweight Management of Resource-Constrained Sensor Devices in Internet of Things

Abstract: It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.

Decentralized Traffic Aware Scheduling in 6TiSCH Networks: Design and Experimental Evaluation

Abstract: This paper capitalizes on two emerging trends, i.e., the growing use of wireless at the edge of industrial control networks and the growing interest to integrate IP into said networks. This is facilitated by recent design contributions from the IEEE and the IETF, where the former developed a highly efficient deterministic time–frequency scheduled medium access control protocol in the form of IEEE 802.15.4e timeslotted channel hopping (TSCH) and the latter IPv6 networking paradigms in the form of 6LoWPAN/ROLL, and scheduling approaches in the form of 6TiSCH. The focus of the present work is on advancing the state-of-the-art of deterministic 6TiSCH schedules toward more flexible but equally reliable distributed approaches. In addition, this paper aims to introduce the first implementation of 6TiSCH networks for factory automation environments: it outlines the challenges faced to overcome the scalability issues inherent to multihop dense low-power networks; the experimental results confirm that the naturally unreliable radio medium can support time-critical and reliable applications. These developments pave the way for wireless industry-grade monitoring approaches.

A Crowdsourcing Assignment Model Based on Mobile Crowd Sensing in the Internet of Things

Abstract: With the powerful sensing capability of mobile smart devices, users can easily obtained the crowd sensing services with smart devices in the Internet of Things (IoT). However, credible interaction issues between mobile users are still the hard problems in the past. In this paper, we focus on how to assign the crowdsourcing sensing tasks based on the credible interaction between users. First, a novel credible crowdsourcing assignment model is proposed based on social relationship cognition and community detection. Second, the service quality factor (SQF), link reliability factor (LRF), and region heat factor (RHF) are introduced to scientifically evaluate the user crowdsourcing preferences. Then, a crowdsourcing algorithm based on analytic hierarchy process (AHP) theory is proposed. Finally, the simulation experiments prove the correctness, effectiveness, and robustness of our method.

A Context-Aware Trust-Based Information Dissemination Framework for Vehicular Networks

Abstract: Reliable, secure, private, and fast communication in vehicular networks is extremely challenging due to the highly mobile nature of these networks. Contact time between vehicles is very limited and topology is constantly changing. Trusted communication in vehicular networks is of crucial importance because without trust, all efforts for minimizing the delay or maximizing the reliability could be voided. In this paper, we propose a trust-based framework for a safe and reliable information dissemination in vehicular networks. The proposed framework consists of two modules such that the first one applies three security checks to make sure the message is trusted. It assigns a trust value to each road segment and one to each neighborhood, instead of each car. Thus, it scales up easily and is completely distributed. Once a message is evaluated and considered to be trustworthy, our method then in the second module looks for a safe path through which the message is forwarded. Our frameworks are application-centric; in particular, it is capable of preserving traffic requirements specified by each application. Experimental results demonstrate that this framework outperforms other well-known routing protocols since it routes the messages via trusted vehicles.

Predictable Low-Latency Event Detection With Parallel Complex Event Processing

Abstract: The tremendous number of sensors and smart objects being deployed in the Internet of Things (IoT) pose the potential for IT systems to detect and react to live-situations. For using this hidden potential, complex event processing (CEP) systems offer means to efficiently detect event patterns (complex events) in the sensor streams and therefore, help in realizing a “distributed intelligence” in the IoT. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is crucial to limit the time events need to be buffered before they actually can be processed. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism in detecting event patterns, which formerly could only consistently be detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet a buffering limit for event detection under high and dynamic workloads.

A Software Architecture Enabling the Web of Things

Abstract: The Internet of Things (IoT) will include billions of smart “things” connected to the Web and characterized by sensing, actuating, and data processing capabilities. In this context, also known as Web of Things (WoT), the user should ideally be able to collect information provided by smart things, and to mash-up them to obtain value-added services. However, in the current solutions, the access to physical objects is poorly scalable and efficient, the communications are often unidirectional (from the devices to the users), and only tech-savvy people are able to develop mash-up applications. Based on these assumptions, we propose a software architecture to easily mash-up constrained application protocol (CoAP) resources. It is able to discover the available devices and to virtualize them outside the physical network. These virtualizations are then exposed to the upper layers by a REpresentational State Transfer (REST) interface, so that the physical devices interact only with their own virtualization. Furthermore, the system provides simplified tools allowing the development of mash-up applications to different-skilled users. Finally, the architecture allows not only to monitor but also to control the devices, thus establishing a bidirectional communication channel. To evaluate the proposal, we deeply modify and integrate some existing software components to realize an instance of the architecture.

Balanced Cluster Size Solution to Extend Lifetime of Wireless Sensor Networks

Abstract: Clustering algorithms are considered as energy-efficient approach for resource-constraint wireless sensor networks. Traditional clustering algorithms have uneven clusters that make network load unbalanced. In this paper, a clustering approach is proposed to address this problem and it provides balanced clusters by considering thresholds for cluster formation. Simulation results show that proposed solution can offer a better network lifetime with reduction in node death rate when compared to the traditional approaches.

EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art

Abstract: Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its “features” (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions.

Keep Your Promise: Mechanism Design Against Free-Riding and False-Reporting in Crowdsourcing

Abstract: Crowdsourcing is an emerging paradigm where users can have their tasks completed by paying fees, or receive rewards for providing service. A critical problem that arises in current crowdsourcing mechanisms is how to ensure that users pay or receive what they deserve. Free-riding and false-reporting may make the system vulnerable to dishonest users. In this paper, we design schemes to tackle these problems, so that each individual in the system is better off being honest and each provider prefers completing the assigned task. We first design a mechanism EFF which eliminates dishonest behavior with the help from a trusted third party for arbitration. We then design another mechanism DFF which, without the help from any third party, discourages dishonest behavior. We also prove that DFF is semi-truthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing transaction-wise budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no user could have a utility gain by unilaterally being dishonest.

Body Node Coordinator Placement Algorithms for Wireless Body Area Networks

Abstract: Wireless body area networks (WBANs) are intelligent wireless monitoring systems, consisting of wearable, and implantable computing devices on or in the human body. They are used to support a variety of personalized, advanced, and integrated applications in the field of medical, fitness, sports, military, and consumer electronics. In a WBAN, network longevity is a major challenge due to the limitation of the availability of energy supply in body nodes. Therefore, routing protocols can play a key role towards making such networks energy efficient. In this work, we exhibit that a routing protocol together with an effective body node coordinator (BNC) deployment strategy can influence the network lifetime eminently. Our initial work shows that the variation in the placement of a BNC within a WBAN could significantly vary the overall network lifetime. This motivated us to work on an effective node placement strategy for a BNC, within a WBAN; and thus we propose three different BNC placement algorithms considering different features of available energy efficient routing protocols in a WBAN. Our simulation results show that these algorithms along with an appropriate routing protocol can prolong the network lifetime by up to 47.45%.

A Novel Wireless Sensor Network Frame for Urban Transportation

Abstract: The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.

PWDGR: Pair-Wise Directional Geographical Routing Based on Wireless Sensor Network

Abstract: Multipath routing in wireless multimedia sensor network makes it possible to transfer data simultaneously so as to reduce delay and congestion and it is worth researching. However, the current multipath routing strategy may cause problem that the node energy near sink becomes obviously higher than other nodes which makes the network invalid and dead. It also has serious impact on the performance of wireless multimedia sensor network (WMSN). In this paper, we propose a pair-wise directional geographical routing (PWDGR) strategy to solve the energy bottleneck problem. First, the source node can send the data to the pair-wise node around the sink node in accordance with certain algorithm and then it will send the data to the sink node. These pair-wise nodes are equally selected in 360° scope around sink according to a certain algorithm. Therefore, it can effectively relieve the serious energy burden around Sink and also make a balance between energy consumption and end-to-end delay. Theoretical analysis and a lot of simulation experiments on PWDGR have been done and the results indicate that PWDGR is superior to the proposed strategies of the similar strategies both in the view of the theory and the results of those simulation experiments. With respect to the strategies of the same kind, PWDGR is able to prolong 70% network life. The delay time is also measured and it is only increased by 8.1% compared with the similar strategies.

MASK-BAN: Movement-Aided Authenticated Secret Key Extraction Utilizing Channel Characteristics in Body Area Networks

Abstract: Recently, most wireless network security schemes merely based on physical layer characteristics tackle the two fundamental issues-device authentication and secret key extraction separately. It remains an open problem to simultaneously achieve device authentication and fast secret key extraction merely using wireless physical layer characteristics, without the help of advanced hardware or out-of-band channel. In this paper, we answer this open problem in the setting of wireless body area networks (BANs). We propose MASK-BAN, a lightweight fast authenticated secret key extraction scheme for intra-BAN communication. Our scheme neither introduces advanced hardware nor relies on out-of-band channels. To perform device authentication and fast secret key extraction at the same time, we exploit the heterogeneous channel characteristics among the collection of on-body channels during body motion. On one hand, MASK-BAN achieves authentication through multihop stable channels, which greatly reduces the false positive rate as compared to existing work. On the other hand, based on dynamic channels, key extraction between two on-body devices with multihop relay nodes is modeled as a max-flow problem, and a novel collaborative secret key generation algorithm is introduced to maximize the key generation rate. Extensive real-world experiments on low-end commercial-off-the-shelf sensor devices validate MASK-BAN's great authentication capability and high-secret key generation rate.

Investigating Public-Key Certificate Revocation in Smart Grid

Abstract: The public key cryptography (PKC) is essential for securing many applications in smart grid. For the secure use of the PKC, certificate revocation schemes tailored to smart grid applications should be adopted. However, little work has been done to study certificate revocation in smart grid. In this paper, we first explain different motivations that necessitate revoking certificates in smart grid. We also identify the applications that can be secured by PKC and thus need certificate revocation. Then, we explain existing certificate revocation schemes and define several metrics to assess them. Based on this assessment, we identify the applications that are proper for each scheme and discuss how the schemes can be modified to fully satisfy the requirements of its potential applications. Finally, we study certificate revocation in pseudonymous public key infrastructure (PPKI), where a large number of certified public/private keys are assigned for each node to preserve privacy. We target vehicles-to-grid communications as a potential application. Certificate revocation in this application is a challenge because of the large number of certificates. We discuss an efficient certificate revocation scheme for PPKI, named compressed certificate revocation lists (CRLs). Our analytical results demonstrate that one revocation scheme cannot satisfy the overhead/security requirements of all smart grid applications. Rather, different schemes should be employed for different applications. Moreover, we used simulations to measure the overhead of the schemes.

A New Method for Profile Generation in an Internet of Things Environment: An Application in Ambient-Assisted Living

Abstract: Ambient-assisted living (AAL) is currently one of the important research and development areas, where accessibility, usability, and learning play a major role and where future interfaces are an important concern for applied engineering. The general goal of AAL solutions is to apply ambient intelligence technology to enable people with specific demands, e.g., handicapped or elderly, to live in their preferred environment longer. The term “Internet of Things” (IoT) is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. In this context, we propose a new methodology based on self organizing maps (SOMs) and fuzzy C-means (FCM) algorithms for profile generation as regards the activities of the user and their correlation with the available sensors. Moreover, we utilize the provided context to assign the generated profiles to more contextually complex activities. Our methodology is being evaluated into an AAL structure equipped with several sensors. More precisely, we assess the proposed method in a data set generated by accelerometers and its performance over a number of everyday activities.

Implementation of Multiparameter Monitoring by an LC -Type Passive Wireless Sensor Through Specific Winding Stacked Inductors

Abstract: A traditional $LC$ -type passive wireless sensor has been used for the measurement in sealed environments. Due to the limitation of its operation principle, this method was only suitable for a single parameter monitoring. For most applications, it is desirable for multiparameters to be monitored. In order to keep the chip area of the sensor as small as possible, multilevel inductors may be coaxially stacked. However, the transmitting signals affect each other due to strong mutual coupling between the stacked inductors. This paper presents a novel inductor structure by using a specific winding to achieve the minimized mutual inductance. Using the partial inductance theory, the mutual inductance of two stacked inductors is analyzed. Simulations of the two stacked inductors coaxially aligned, with each connected in a variable capacitor, show that the mutual inductance between the two inductors can be greatly suppressed. This phenomenon has also been verified through multilayer printed circuit board (PCB) inductors. Capacitive temperature and pressure sensors were linked to the two stacked inductor to implement the simultaneous measurements of temperature and pressure. The measurement results indicate that the sensitivity of the temperature sensor is about 41.67 kHz/°C between ${\mathbf -20} \; {^{\circ}{\mathbf{C}}}$ and 100°C, while the sensitivity of the pressure sensor is about ${\mathbf -133.33}\;{\mathbf{kHz}} / {\mathbf{kPa}}$ between 50 and 110 kPa.

PRLS-INVES: A General Experimental Investigation Strategy for High Accuracy and Precision in Passive RFID Location Systems

Abstract: Due to cost-effectiveness and easy-deployment, radio-frequency identification (RFID) location systems are widely utilized into many industrial fields, particularly in the emerging environment of the Internet of Things (IoT). High accuracy and precision are key demands for these location systems. Numerous studies have attempted to improve localization accuracy and precision using either dedicated RFID infrastructures or advanced localization algorithms. But these effects mostly consider utilization of novel RFID localization solutions rather than optimization of this utilization. Practical use of these solutions in industrial applications leads to increased cost and deployment difficulty of RFID system. This paper attempts to investigate how accuracy and precision in passive RFID location systems (PRLS) are impacted by infrastructures and localization algorithms. A general experimental-based investigation strategy, PRLS-INVES, is designed for analyzing and evaluating the factors that impact the performance of a passive RFID location system. Through a case study on passive high frequency (HF) RFID location systems with this strategy, it is discovered that: 1) the RFID infrastructure is the primary factor determining the localization capability of an RFID location system and 2) localization algorithm can improve accuracy and precision, but is limited by the primary factor. A discussion on how to efficiently improve localization accuracy and precision in passive HF RFID location systems is given.