The Internet of Things vision: Key features, applications and open issues

Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

RMST: Reliable Data Transport in Sensor Networks

Dynamic spectrum access stands as a promising and spectrum-efficient communication approach for resource-constrained multihop wireless sensor networks due to their event-driven communication nature, which generally yields bursty traffic depending on the event characteristics. In addition, opportunistic spectrum access may also help realize the deployment of multiple overlaid sensor networks, and eliminate collision and excessive contention delay incurred by dense node deployment. Incorporating cognitive radio capability in sensor networks yields a new sensor networking paradigm (i.e., cognitive radio sensor networks). In this article the main design principles, potential advantages, application areas, and network architectures of CRSNs are introduced. The existing communication protocols and algorithms devised for cognitive radio networks and WSNs are discussed along with the open research avenues for the realization of CRSNs.

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Cognitive radio sensor networks

A wireless sensor network with multimedia capabilities typically consists of data sensor nodes, which sense, for instance, sound or motion, and video sensor nodes, which capture video of events of interest. In this survey, we focus on the video encoding at the video sensors and the real-time transport of the encoded video to a base station. Real-time video streams have stringent requirements for end-to-end delay and loss during network transport. In this survey, we categorize the requirements of multimedia traffic at each layer of the network protocol stack and further classify the mechanisms that have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack. Specifically, we consider the mechanisms operating at the application, transport, network, and MAC layers. We also review existing cross-layer approaches and propose a few possible cross-layer solutions to optimize the performance of a given wireless sensor network for multimedia streaming applications.

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A survey of multimedia streaming in wireless sensor networks

The pervasiveness of wireless technology has indeed created massive opportunity to integrate almost everything into the Internet fabric. This can be seen with the advent of Internet of Things and Cyber Physical Systems, which involves cooperation of massive number of intelligent devices to provide intelligent services. Fairness amongst these devices is an important issue that can be analysed from several dimensions, e.g., energy usage, achieving required quality of services, spectrum sharing, and so on. This article focusses on these viewpoints while looking at fairness research. To generalize, mainly wireless networks are considered. First, we present a general view of fairness studies, and pose three core questions that help us delineate the nuances in defining fairness. Then, the existing fairness models are summarized and compared. We also look into the major fairness research domains in wireless networks such as fair energy consumption control, power control, topology control, link and flow scheduling, channel assignment, rate allocation, congestion control and routing protocols. We make a distinction amongst fairness, utility and resource allocation to begin with. Later, we present their inter-relation. At the end of this article, we list the common properties of fairness and give an example of fairness management. Several open research challenges that point to further work on fairness in wireless networks are also discussed. Indeed, the research on fairness is entangled with many other aspects such as performance, utility, optimization and throughput at the network and node levels. While consolidating the contributions in the literature, this article tries to explain the niceties of all these aspects in the domain of wireless networking.

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Fairness in Wireless Networks:Issues, Measures and Challenges

In this article we present a survey of transport protocols for wireless sensor networks (WSNs). We first highlight several unique aspects of WSNs, and describe the basic design criteria and challenges of transport protocols, including energy-efficiency, quality of service, reliability, and congestion control. We then provide a summary and comparison of existing transport protocols for WSNs. Finally, we discuss several open research problems.

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A survey of transport protocols for wireless sensor networks

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.

Upstream congestion control in wireless sensor networks through cross-layer optimization

In wireless sensor networks (WSNs), congestion occurs, for example, when nodes are densely distributed, and/or the application produces high flow rate near the sink due to the convergent nature of upstream traffic. Congestion may cause packet loss, which in turn lowers throughput and wastes energy. Therefore congestion in WSNs needs to be controlled for high energy-efficiency, to prolong system lifetime, improve fairness, and improve quality of service (QoS) in terms of throughput (or link utilization) and packet loss ratio along with the packet delay. This paper proposes a node priority-based congestion control protocol (PCCP) for wireless sensor networks. In PCCP, node priority index is introduced to reflect the importance of each node. PCCP uses packet inter-arrival time along with packet service time to measure a parameter defined as congestion degree and furthermore imposes hop-by-hop control based on the measured congestion degree as well as the node priority index. PCCP controls congestion faster and more energy-efficiency than other known techniques.

Priority-based congestion control in wireless sensor networks

The paper presents a survey on transport control protocols for wireless sensor networks (WSNs). First, it lists the disadvantages of traditional transport control protocols (TCP and UDP) for the environment of WSNs. Second, several design issues of transport control protocols for WSNs are presented. Third, some existing transport control protocols for WSNs are classified and compared. Finally, several problems needing further studying are outlined.

Issues of transport control protocols for wireless sensor networks

Mobile sensor networks are desirable in a variety of application scenarios, in which information collection is no doubt of great importance. In this paper, we present a mobile sensor network architecture consisting of a potentially large number of mobile sensors and a single or multiple stationary sink nodes for sensing information collection. We formulate a distinct coverage measurement problem in term of sensing information collection; we study the relevant performance and examine the effect from a variety of relevant factors through extensive simulations. We demonstrate that the performance is not only affected by the sensor mobility and the transmission range between mobile sensors and sink node(s), but also by the distribution of mobile sensors and the number and locations of sink nodes. Based on the observation and analysis, we also provide some preliminary understandings and implications for improving the information collection performance.

Yueming Hu

Papers

A survey of transport protocols for wireless sensor networks

Upstream congestion control in wireless sensor networks through cross-layer optimization

Priority-based congestion control in wireless sensor networks

Issues of transport control protocols for wireless sensor networks

The Sink Node Placement and Performance Implication in Mobile Sensor Networks