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.

/pdf/a-survey-of-multi-objective-optimization-in-wireless-sensor-329pcv7jcj.pdf

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

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 trade-offs 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.

/pdf/a-survey-of-multi-objective-optimization-in-wireless-sensor-44bohippw3.pdf

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

Prediction of short-term PV power output and uncertainty analysis

An incentive mechanism with privacy protection in mobile crowdsourcing systems

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

https://www.mdpi.com/1424-8220/15/7/17572/pdf

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.1546

An efficient privacy preserving data aggregation approach for mobile sensing

The social activities of Primary Users (PUs) and Secondary Users (SUs) affect actual accessible whitespace in Cognitive Radio Networks (CRNs). However, the impacts of primary activities on available whitespace have been extensively investigated due to the dominating priority of PUs, while the impacts of secondary activities on actual accessible whitespace have been ignored. Therefore, we propose to incorporate the primary and secondary activities in the analysis and decision of the accessible whitespace, namely, both the dominance of PUs over SUs and the competitions among SUs are simultaneously taken into account. Specifically, we first approximate primary activity probability based on the real datasets of mobile phone usage records, then the spectrum opportunity between a pair of communication SUs is deduced based on primary activities. Next, we infer the access probability limit of SUs successfully accessing the whitespace according to the primary activity probability, and depict the secondary activity probability from the views of social activity patterns and social networks respectively. Furthermore, the actual accessible probability of whitespace is given by introducing the competitions among SUs. Finally, a greedy routing algorithm, considering the accessible whitespace and the distance to the destination, is proposed to verify our idea. The experiment results based on the real datasets demonstrate the correctness of our analysis and the advantages of the proposed algorithm.

User social activity-based routing for cognitive radio networks

Power loss and interference coexist in wireless transmissions where random uncertainty is aggravated due to the mobility of sensor nodes. A probability interference model was proposed, based on the physical model and random fading of the received signal power, to depict the uncertainty of wireless interference. In addition, an interference-aware routing metric was designed, in which interference, routing convergence and residual energies of nodes were integrated. Furthermore, an interference-aware probabilistic routing algorithm was proposed for mobile wireless sensor networks, and its correctness and time and space complexities were proved. The NS-2 simulation experiments showed that the proposed algorithm can achieve higher packet delivery ratio than Greedy Perimeter Stateless Routing in various cases like the pause time and maximum moving speed. Simultaneously, the energy consumption of a packet and average delay were taken into consideration to better meet the needs of mobile scenarios with higher reliability.

Signal power random fading based interference-aware routing for wireless sensor networks

A QoS-aware and priority-based multipath routing (QoSPMR) algorithm is proposed for WMSNs. The proposed algorithm comprehensively considers the categories and priorities of multimedia streams, the residual energy of nodes, the local link reliability between nodes, the local congestion status and the distance from the candidate next hop nodes to the sink. Simulation results show the QoSPMR algorithm is more effective than the TPGF algorithm.

QoSPMR: QoS-Aware and Priority-Based Multipath Routing Algorithm for WMSNs

Routing is a challenging issue in Delay/Disruption Tolerant Networks (DTNs) due to the characteristics of high mobility of nodes and high dynamicity of network topology. The previous works did not consider the spacial information of nodes, such as dwelling time at a location when predicting the future contact of two nodes. In this paper, a novel Spacial Mobility Prediction based Routing (SMPR) scheme is proposed for DTNs, in which the spacial information of nodes and contact transitivity are both taken into account. Specifically, a time homogeneous semi-Markov process model is proposed to describe the mobility of nodes. The simulation results show that the proposed SMPR scheme substantially improves delivery ratio and reduces delivery latency compared with traditional DTN routing schemes.

Spacial Mobility Prediction Based Routing Scheme in Delay/Disruption-Tolerant Networks

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