Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Even after 16 years of existence, low energy adaptive clustering hierarchy (LEACH) protocol is still gaining the attention of the research community working in the area of wireless sensor network (WSN). This itself shows the importance of this protocol. Researchers have come up with various and diverse modifications of the LEACH protocol. Successors of LEACH protocol are now available from single hop to multi-hop scenarios. Extensive work has already been done related to LEACH and it is a good idea for a new research in the field of WSN to go through LEACH and its variants over the years. This paper surveys the variants of LEACH routing protocols proposed so far and discusses the enhancement and working of them. This survey classifies all the protocols in two sections, namely, single hop communication and multi-hop communication based on data transmission from the cluster head to the base station. A comparitive analysis using nine different parameters, such as energy efficiency, overhead, scalability complexity, and so on, has been provided in a chronological fashion. The article also discusses the strong and the weak points of each and every variants of LEACH. Finally the paper concludes with suggestions on future research domains in the area of WSN.

A Survey on Successors of LEACH Protocol

In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.

https://www.ece.ubc.ca/~wingn/papers/2014/Journal/MOOP_WIPT.pdf

Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer

Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical (ISM) band for transmissions. However, with the increasing usage and demand of these networks, the currently available ISM band does not suffice for their transmissions. This spectrum insufficiency problem has been overcome by incorporating the opportunistic spectrum access capability of cognitive radio (CR) into the existing WSN, thus giving birth to CR sensor networks (CRSNs). The sensor nodes in CRSNs depend on power sources that have limited power supply capabilities. Therefore, advanced and intelligent radio resource allocation schemes are very essential to perform dynamic and efficient spectrum allocation among sensor nodes and to optimize the energy consumption of each individual node in the network. Radio resource allocation schemes aim to ensure QoS guarantee, maximize the network lifetime, reduce the internode and internetwork interferences, etc. In this paper, we present a survey of the recent advances in radio resource allocation in CRSNs. Radio resource allocation schemes in CRSNs are classified into three major categories, i.e., centralized, cluster-based, and distributed. The schemes are further divided into several classes on the basis of performance optimization criteria that include energy efficiency, throughput maximization, QoS assurance, interference avoidance, fairness and priority consideration, and hand-off reduction. An insight into the related issues and challenges is provided, and future research directions are clearly identified.

A Survey on Radio Resource Allocation in Cognitive Radio 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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O. Karli

Papers

Cognitive radio sensor networks