Showing papers on "Key distribution in wireless sensor networks published in 2018"

Combining Solar Energy Harvesting with Wireless Charging for Hybrid Wireless Sensor Networks

Abstract: The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed $1.61(1+\epsilon)^2$ -approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors’ transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles’ moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

Survey on clustering in heterogeneous and homogeneous wireless sensor networks

Abstract: In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay In this paper, several clustering methods are studied to demonstrate advantages and disadvantages of them Among them, some methods deal with homogenous network, whereas some deals with heterogeneous In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Abstract: Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.

TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks

Abstract: The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a t emporal- s patial c harging scheduling a lgorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

Abstract: Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.

Collaborative Energy and Information Transfer in Green Wireless Sensor Networks for Smart Cities

Abstract: Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

Abstract: Summary In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering-based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.

Recent Advances in Attacks, Technical Challenges, Vulnerabilities and Their Countermeasures in Wireless Sensor Networks

Abstract: Advances in hardware manufacturing technology, wireless communications, micro electro-mechanical devices and information processing technologies enabled the development of WSNs. These consist of numerous, low cost, small sensor nodes powered by energy constrained batteries. WSNs have attracted much interest from both industry and academia due to its wide range of applications such as environment monitoring, battlefield awareness, medical healthcare, military investigation and home appliances management. Thus information in sensor network needs to be protected against various attacks. Attackers may employ various security threats making the WSN systems vulnerable and unstable. This paper examines the security threats and vulnerabilities imposed by the distinctive open nature of WSNs. We first summarize the requirements in WSNs that includes both the survivality and security issues. Next, a comprehensive survey of various routing and middleware challenges for wireless networks is presented. Next, paper explores the potential security threats at different protocol layers. Here various security attacks are identified along with their countermeasures that were investigated by different researchers in recent years. We also provide a detailed survey of data aggregation and the energy-efficient routing protocols for WSNS. And finally, few unsolved technical challenges and the future scope for WSN security has been outlined.

Optimizing K-coverage of mobile WSNs

Abstract: Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K -coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%–41.3% using stationary nodes while it was in a range of 29.3%–45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%–17.6%. Moreover, the running time to form the network structure and switch between nodes’ modes is reduced by 12%.

EAPC: Energy-Aware Path Construction for Data Collection Using Mobile Sink in Wireless Sensor Networks

Abstract: Data collection is one of the paramount concerns in wireless sensor networks. Many data collection algorithms have been proposed for collecting data in particular monitoring regions. However, the efficiency of the paths for such data collection can be improved. This paper proposes an energy-aware path construction (EAPC) algorithm, which selects an appropriate set of data collection points, constructs a data collection path, and collects data from the points burdened with data. EAPC is intended to prolong the network lifetime, it accounts for the path cost from its current data collection point to the next point and the forwarding load of each sensor node. Performance evaluation reveals that the proposed EAPC has more efficient performance than existing data collection mechanisms in terms of network lifetime, energy consumption, fairness index, and efficiency index.

A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks

Abstract: Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff. The proposed approach, based on the reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. Simulation results demonstrate the good performance of the proposed approach in various circumstances.

Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

Abstract: The popularity of Wireless Sensor Networks (WSNs) is rapidly growing due to its wide-ranged applications such as industrial diagnostics, environment monitoring or surveillance. High-quality construction of WSNs is increasingly demanding due to the ubiquity of WSNs. The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage. The proposed model is involved with sensor nodes deployment which maximizes the area coverage. This problem is proved to be NP-hard. Although such algorithms to handle this problem with fairly acceptable solutions had been introduced, most of them still heavily suffer from several issues including the large computation time and solution instability. Hence, the existing work proposed ways to overcome such difficulties by proposing two nature-based algorithms, namely Improved Cuckoo Search (ICS) and Chaotic Flower Pollination algorithm (CFPA). By adopting the concept of calculating the adaptability and a well-designed local search in previous studies, those two algorithms are able to improve their performance. The experimental results on 15 instances established a huge enhancement in terms of computation time, solution quality and stability.

A Kernel-Based Compressive Sensing Approach for Mobile Data Gathering in Wireless Sensor Network Systems

Abstract: The recent advances of compressive sensing (CS) have witnessed a great potential of efficient compressive data gathering (CDG) in wireless sensor network systems (WSNSs). However, most existing work on CDG mainly focuses on multihop relaying strategies to improve the performance of data gathering. In this paper, we propose a mobile CDG scheme including a random walk-based algorithm and a kernel-based method for sparsifying sensory data from irregular deployments. The proposed scheme allows a mobile collector to harvest data by sequentially visiting a number of nodes along a random path. More importantly, toward building the gap between CS and machine learning theories, we explore a theoretical foundation for understanding the feasibility of the proposed scheme. We prove that the CS matrices, constructed from the proposed random walk algorithm combined with a kernel-based sparsity basis, satisfy the restricted isometry property. Particularly, we also show that ${m=O}({k\log } ({n/k}))$ measurements collected by a mobile collector are sufficient to recover a ${k}$ -sparse signal and ${t=O(k\log (n/k))}$ steps are required to collect these measurements in a network with ${n}$ nodes. Finally, we also present extensive numerical results to validate the effectiveness of the proposed scheme by evaluating the performance in terms of energy consumption and the impact of packet losses. The numerical results demonstrate that the proposed scheme is able to not only significantly reduce communication cost but also combat unreliable wireless links under various packet losses compared to the state-of-the-art schemes, which provides an efficient alternative to data relaying approaches for CDG in WSNS.

Energy-Aware Clustering-Based Routing in Wireless Sensor Networks Using Cuckoo Optimization Algorithm

Abstract: Since sensor nodes make use of battery energy, energy consumption and limitation of sensor nodes is regarded as a fundamental challenge and problem in wireless sensor nodes. Recently, in wireless sensor networks (WSNs), clustering-based energy-aware routing protocols divide neighboring nodes into separate clusters and select local cluster heads so as to combine and transmit information of each of the clusters to the central station. In this way, they attempt to maintain energy consumption balance by the network nodes. When compared with other methods, clustering methods have been able to achieve the best efficiency with regard to the enhancement of network lifetime. In this paper, using cuckoo optimization algorithm, an energy-aware clustering-based routing protocol was proposed in WSNs which is able to cluster the network and select optimal cluster heads. The proposed method considered four criteria with regard to selecting cluster heads in the targeted cuckoo algorithm, namely the remaining energy of nodes, distance to the base station, within-cluster distances and between cluster distances. The results of simulating the proposed method in Matlab environment indicated it is better than other algorithms such as low energy adaptive clustering hierarchical (LEACH), application-specific low power routing, LACH-EP and LEACH with distance-based threshold with regard to the first node die on average and packet delivery rate for six scenario.

Self-Powered ZigBee Wireless Sensor Nodes for Railway Condition Monitoring

Abstract: A track-borne energy transducer is a smart device for harvesting energy of trains or rail transportation systems. In this paper, the authors extend this application through introducing a self-powered ZigBee wireless sensor node. The proposed hardware prototype consists of a ZigBee coordinator at road-side and a series of sensors (Accelerometer, temperature sensor, humidity sensor, and infrared detector) connected to a ZigBee end device at rail-side. The ZigBee end device is powered by the magnetic levitation energy harvester and communicated wirelessly with the ZigBee coordinator. The magnetic levitation oscillator is selected due to its broad-band response characteristics. The results indicate a peak–peak output voltage of 2.3 V under the condition that the vehicle travels over the rail-borne device at the speed of 105 km/h.

Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks

Abstract: In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) by trading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays under two scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation, and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power, and power splitting ratio.

Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization

Abstract: 3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods.

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Abstract: Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Heterogeneous fault diagnosis for wireless sensor networks

Abstract: Fault diagnosis has been considered as a very challenging problem in wireless sensor network (WSN) research. Faulty nodes having different behavior such as hard, soft, intermittent, and transient fault are called as heterogeneous faults in wireless sensor networks. This paper presents a heterogeneous fault diagnosis protocol for wireless sensor networks. The proposed protocol consists of three phases, such as clustering phase, fault detection phase, and fault classification phase to diagnose the heterogeneous faulty nodes in the wireless sensor networks. The protocol strategy is based on time out mechanism to detect the hard faulty nodes, and analysis of variance method (ANOVA test) to detect the soft, intermittent, and transient faulty nodes in the network. The feed forward probabilistic neural network (PNN) technique is used to classify the different types of faulty nodes in the network. The performance of the proposed heterogeneous fault diagnosis protocol is evaluated using network simulator NS-2.35. The evaluation of the proposed model is also carried out by the testbed experiment in an indoor laboratory environment and outdoor environment.

An Efficient Minimum-Latency Collision-Free Scheduling Algorithm for Data Aggregation in Wireless Sensor Networks

Abstract: Data collection is one of the most important operations in applications of wireless sensor networks (WSNs). In many emerging WSN applications, it is urgent to achieve a guarantee for the latency involved in collecting data. Many researchers have studied collecting data in WSNs with minimum latency but without data collision while assuming that any (or no) data are allowed to be aggregated into one packet. In addition, tree structures are often used for solutions. However, in some cases, a fixed number of data are allowed to be aggregated into one packet. This motivates us to study the problem of minimizing the latency for data aggregation without data collision in WSNs when a fixed number of data are allowed to be aggregated into one packet, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling (MLCAMDAS) problem. The MLCAMDAS problem is shown to be NP-complete here. In addition, a nontree-based method, termed the independent-set-based collision-avoidance scheduling (ISBCAS) algorithm, is proposed accordingly. The ISBCAS is demonstrated via simulations to have good performance.

Roadside Magnetic Sensor System for Vehicle Detection in Urban Environments

Abstract: Intelligent Transportation Systems (ITS) are widely researched to improve the traffic situation. In ITS, vehicle detection system plays a significant role. At present, vehicle detection is often conducted by inductive loops, which are very expensive and inconvenient to install and maintain. Video camera is another frequently used detector, but it needs high computing power. In order to solve these problems, this paper focuses on the development of a roadside magnetic sensor system for vehicle detection. The device is installed at the side of the road and measures traffic in the adjacent lane (the closest lane to the sensor node). The data are transmitted by the IEEE 802.15.4 communication protocol. A novel adapted threshold state machine algorithm is proposed to detect vehicles. Since false judgments created by large vehicles passing in the nonadjacent lane (the lane next to the closest lane to the sensor node) are frequent in urban environments, a novel feature extracted by fusion of three magnetic sensor signals are proposed to reduce this error. The developed magnetic sensor system is wireless, compact, and cost-effective. The experimental results show that the proposed system achieves high accuracy and is viable in urban environments.

A Cooperative Clustering Protocol With Duty Cycling for Energy Harvesting Enabled Wireless Sensor Networks

Abstract: This paper proposes a cooperative clustering protocol based on the low energy adaptive clustering hierarchy approach to enhance the longevity of energy harvesting-based wireless sensor networks (EH-WSN). In the proposed protocol, to ensure that any energy consumption associated with the role of the cluster head (CH) is shared between the nodes, the CH role is alternated between the nodes using duty cycling as a function of their individual energy harvesting capabilities. Furthermore, to maintain an energy neutral operation when not acting as a CH, the nodes adopt a data transmission duty cycle and any excess energy is invested in relaying other nodes’ packets. To optimize the relaying performance, a novel cross-layer cooperative TDMA scheme is also presented. The optimal number of clusters in an EH-WSN is analyzed in terms of energy consumption, latency, and bandwidth utilization. Simulations, performed using GreenCastalia, demonstrate tangible performance enhancements in adopting the proposed protocol over benchmark schemes in terms of throughput and lifetime, particularly under highly constrained energy conditions.

Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks

Abstract: Load balance is a vital goal for battery-powered wireless sensor networks (WSNs). In this letter, we propose a novel load balancing strategy for data transmission of WSNs, namely, super links-based data drainage, which makes full use of the advantages of super nodes with more powerful hardware and greater communication capacity to realize data traffic redistribution. Being different from conventional passive late-remedy approaches, this is a positive and early-intervention strategy. Specifically, an evaluation function is designed to select appropriate start points and end points of super links, and the core idea is to transfer data from locations relatively far from the sink with a jump of data traffic to those near the sink with little data traffic. Extensive simulations are conducted to validate the effectiveness and advantages of the new strategy.

Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

Abstract: Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

Wireless Sensor Network: A Survey

Abstract: Internet of Things is a proposed germinates of internet in which everyday objects had network connectivity, allowing them to send and receive patient data. Wireless sensor network (WSN) refers a group of stereo metrically scattered and devoted sensors for observing and recording the physical conditions of ambience and formulate the collected data at central location. Though tremendous work has been done in the field of IOT concept wireless sensor network plays an important role in monitoring the patients and providing a medication. It is hindered from being beneficial to quality of services. This paper provides a survey on various techniques to address the challenges in wireless sensor networks.

Energy-Efficient Connected Target Coverage in Multi-hop Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) employ numerous sensor nodes possessing sensing, processing, and wireless communication abilities to monitor a specified sensing field. As sensor nodes are mostly battery operated and are highly constrained regarding energy resources, it is essential to explore energy optimization methods to prolong WSN lifetime. Target tracking is a very conventional WSN application that demands both useful and coherent energy management. This paper proposes a distributed shortest path data collection algorithm for connected target coverage to maximize WSN lifetime pertaining to both static and mobile multi-hop WSNs. The performance is evaluated in TinyOS employing the TOSSIM simulator based on the parameters like percentage of alive nodes, load distribution of nodes, and network lifetime.

Efficient authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks

Abstract: In current times, multimedia application includes integrated sensors, mobile networks and Internet-of-Things (IoT) services. In IoT services, if more devices are connected without much constrains, the problem of security, trust and privacy remain a challenge. For multimedia communications through Wireless Sensor Network (WSN), sensor nodes transmit confidential data to the gateway nodes via public channels. In such an environment, the security remains a serious issue from past many years. Only few works are available to support secure multimedia communications performed in IoT-enabled WSNs. Among the few works, Kumari and Om recently proposed an authentication protocol for multimedia communications in IoT-enabled WSNs, which is applicable in coal mine for safety monitoring. The authors claimed in their work that their contributory protocol strongly withstands several security threats such as, user impersonation attack, sensor node impersonation attack, sensor node anonymity issue and others technical design issues. However, this article proved that Kumari and Om’s protocol has some design flaws and is susceptible to various security attacks including, user and sensor node impersonation attacks. As a remedy, a robust authentication protocol using smartcard is constructed to solve the security issues found in Kumari and Om’s protocol. The proof of correctness of mutual authentication is performed using the BAN logic model. In addition, our further security investigation claimed strong protection against known security attacks. Our protocol is analyzed comprehensively and compared against the similar protocols and the results showed that it is efficient and robust than earlier protocols.

Survey on security in intra-body area network communication

Abstract: With the advances in microelectronics, embedded computing, and wireless communications, the interest in Body Sensor Networks has risen sharply and has enabled the development and implementation of such networks. A Body Sensor Network is constructed from sensor nodes distributed in and on the user's body. The nodes form a wireless network that collects physiological data and forwards it on. This sort of network has wide application prospects in the future of healthcare. The collected data is highly private and must, therefore, be protected adequately. The security mechanisms usually deployed depend heavily on the key agreement scheme. Because of the reliability requirements, energy efficiency, and hardware constraints, building a key agreement scheme for a Body Sensor Network can be quite a challenge. This paper presents a state-of-the-art overview of security in Body Sensor Networks, focusing on proposed key agreement schemes, ways they are built in, and the methods used to evaluate their security and performance. Results show that the community is very much split between the traditional key agreement schemes and schemes that take advantage of physiological or other signals to exchange a key. Security analysis is rarely performed with formal methods; instead, descriptive analysis is commonplace. There are no standards or guidelines on measuring a scheme`s efficiency. The authors therefore used different methods and, consequently, schemes can be difficult to compare.

Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks

Abstract: Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the clustering approach is the imposed energy overhead caused by the “global” clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical clustering-task scheduling policy (HCSP), which triggers node-driven clustering as opposed to GRBP's time-driven clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable clustering-task scheduling than that of GRBP. This policy mitigates the clustering overhead, which is the worst disadvantage of clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.