Support of multiple sinks via a virtual root for the RPL routing protocol

Data acquisition in large wireless sensor networks consisting of only a single sink can typically lead to scalability and energy efficiency issues. A solution to this problem is the deployment of multiple sinks in the network. This approach is however not supported by the popular sensor network routing protocol, IPv6 routing protocol for low-power and lossy networks (RPL). This paper describes a method to support the usage of multiple sinks for RPL in accordance to the limited guidelines of RPL:IPv6 Routing Protocol for Low-Power and Lossy Networks (RFC 6550). Hereby this paper shows that the concept of a virtual root can work and can be implemented with a minimal complexity. The correct behaviour of this extension was verified, by performance tests, in both a simulation environment and a real-life environment (iMinds wiLab.t office testbed). The chosen approach has the advantage that for an existing deployment of a RPL network, only the sink nodes need to be adapted. The results confirm that the use of multiple sinks in RPL can deliver the desired advantages. For an increase in the number of sinks from 1 to 4, a decrease of about 45% in the maximal and more than 30% in the average energy consumption was obtained in simulations for the used topology. For the real-life tests, the average energy consumption decreased with more than 30% and with more than 50% for the maximal energy consumption when the number of sinks was increased from 1 to 2 on the iMinds wiLab. t office testbed. By using a positioning algorithm to determine the optimal position, for the sinks, possibly even better performances can be obtained

Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm

Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.<br /

Relocating sensor nodes to maximize cumulative connected coverage in wireless sensor networks

PubMed ID: 27879850In order to extend the availability of the wireless sensor network and to extract maximum possible information from the surveillance area, proper usage of the power capacity of the sensor nodes is important. Our work describes a dynamic relocation algorithm called MaxNetLife, which is mainly based on utilizing the remaining power of individual sensor nodes as well as properly relocating sensor nodes so that all sensor nodes can transmit the data they sense to the sink. Hence, the algorithm maximizes total collected information from the surveillance area before the possible death of the sensor network by increasing cumulative connected coverage parameter of the network. A deterministic approach is used to deploy sensor nodes into the sensor field where Hexagonal Grid positioning is used to address and locate each sensor node. Sensor nodes those are not planned to be actively used in the close future in a specific cell are preemptively relocated to the cells those will be in need of additional sensor nodes to improve cumulative connected coverage of the network. MaxNetLife algorithm also includes the details of the relocation activities, which include preemptive migration of the redundant nodes to the cells before any coverage hole occurs because of death of a sensor node. Relocation Model, Data Aggregation Model, and Energy model of the algorithm are studied in detail. MaxNetLife algorithm is proved to be effective, scalable, and applicable through simulations.Publisher's Versio

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri

Energy Aware Algorithms for managing Wireless Sensor Networks

While the majority of the current Wireless Sensor Networks (WSNs) research has prioritized either the coverage of the monitored area or the energy efficiency of the network, it is clear that their relationship must be further studied in order to find optimal solutions that balance the two factors. Higher degrees of redundancy can be attained by increasing the number of active sensors monitoring a given area which results in better performance. However, this in turn increases the energy being consumed. In our research, we focus on attaining a solution that considers several optimization parameters such as the percentage of coverage, quality of coverage and energy consumption. The problem is modeled using a bipartite graph and employs an evolutionary algorithm to handle the activation and deactivation of the sensors. An accelerated version of the algorithm is also presented; this algorithm attempts to cleverly mutate the string being considered after analyzing the desired output conditions and performs a calculated crossover depending on the fitness of the parent strings. This results in a quicker convergence and a considerable reduction in the search time for attaining the desired solutions. Proficient cluster formation in wireless sensor networks reduces the total energy consumed by the network and prolongs the life of the network. There are various clustering approaches proposed, depending on the application and the objective to be attained. There are situations in which sensors are randomly dispersed over the area to be monitored. In our research, we also propose a solution for such scenarios using heterogeneous networks where a network has to self-organize itself depending on the physical allocations of sensors, cluster heads etc. The problem is modeled using a multi-stage graph and employs combinatorial algorithms to determine which cluster head a particular sensor would report to and which sink node a cluster head would report to. The solution proposed provides flexibility so that it can be applied to any network irrespective of density of resources deployed in the network. Finally we try to analyze how the modification of the sequence of execution of the two methods modifies the results. We also attempt to diagnose the reasons responsible for it and conclude by highlighting the advantages of each of the sequence