In this paper we present new optimization formulations for maximizing the network lifetime in wireless mesh networks performing data aggregation and dissemination for machine-to-machine communication in the Internet of Things. We focus on heterogeneous networks in which multiple applications co-exist and nodes may take on different roles for different applications. Moreover, we address network reconfiguration as a means to increase the network lifetime, in keeping with the current trend towards software defined networks and network function virtualization. To test our optimization formulations, we conducted a numerical study using randomly-generated mesh networks from 10 to 30 nodes, and showed that the network lifetime can be increased using network reconfiguration by up to 75 over a single, minimal-energy configuration. Further, our solutions are feasible to implement in practical scenarios: only few configurations are needed, thus requiring little storage for a standalone network, and the synchronization and signalling needed to switch configurations is low relative to each configuration's operating time.
Some work in the literature also considers in-network processing. In @cite_8 , data aggregation trees are constructed and scheduled, and the network can be reconfigured, in that different trees can be used in different time periods. This work again uses the traditional WSN model of many homogeneous sensor nodes all sending measurements to a single sink. The scenario considered in @cite_23 focuses on a machine-to-machine communication application similar to the one we consider, including the presence of edge nodes in the network. However, there, the problem addressed is that of data placement on these edge nodes in order to maximize the network lifetime under latency constraints. Routing is performed by selecting the paths that yield the maximum lifetime, defined as the time until any node runs out of energy; reconfiguration of the network as we propose in this paper is not considered.
@cite_23 @cite_8
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Established approaches to data aggregation in wireless sensor networks (WSNs) do not cover the variety of new use cases developing with the advent of the Internet of Things (IoT). In particular, the current push toward fog computing, in which control, computation, and storage are moved to nodes close to the network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms. Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks. We consider optimization of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes. Although aggregation only accounts for less than 15 of total energy usage in all cases tested, it provides substantial energy savings. Our results show more than 13 times greater energy usage for 40-node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions. In a Wireless Sensor Network (WSN) the sensed data must be gathered and transmitted to a base station where it is further processed by end users. Since that kind of network consists of low-power nodes with limited battery power, power efficient methods must be applied for node communication and data gathering in order to achieve long network lifetimes. In such networks where in a round of communication many sensor nodes have data to send to a base station, it is very important to minimize the total energy consumed by the system so that the total network lifetime is maximized. The lifetime of such sensor network is the time until base station can receive data from all sensors in the network. In this work1, besides the conventional protocol of direct transmission or the use of dynamic routing protocols proposed in literature that potentially aggregates data, we propose an algorithm based on static routing among sensor nodes with unequal energy distribution in order to extend network lifetime and find a near-optimal node energy charge scheme that leads to both node and network lifetime prolongation. Our simulation results show that our algorithm achieves longer network lifetimes mainly because the final energy charge of each node is not uniform, while each node is free from maintaining complex route information and thus less infrastructure communication is needed. Energy consumption is a crucially important issue in battery-driven wireless sensor networks (WSNs). In most sensor networks, the sensors near the data collector (i.e. the sink) become drained more quickly than those elsewhere in the network since they are required to relay all of the data collected in the network to the sink. Therefore more balanced data paths to the sink should be established in order to extend the lifetime of the sensor network. Accordingly, a novel relay deployment scheme for WSNs based on the Voronoi diagram is proposed. The proposed scheme is applicable to both two-dimensional and three-dimensional network topologies and establishes effective routing paths that balance the traffic load within the sensor network and alleviate the burden on the sensors around the sink. Simulation results indicate that the number of relays deployed in the proposed scheme is similar to that deployed in the predetermined location scheme and is significantly less than that deployed in the minimum set cover scheme. Furthermore, the lifetime of the sensor network containing relay nodes deployed using the current scheme is longer than that achieved using either the predetermined location scheme or the minimum set cover scheme. Abstract Energy is one of the scarcest resources in wireless sensor network (WSN). One fundamental way of conserving energy is judicious deployment of sensor nodes within the network area so that energy flow remains balanced throughout the network. This avoids the problem of occurrence of ‘energy holes’ and ensures prolonged network lifetime. We have first investigated the problem for enhancing network lifetime using homogeneous sensor nodes. From our observation it is revealed that energy imbalance in WSN occurs due to relaying of data from different parts of the network towards sink. So for improved energy balance instead of using only sensor nodes it is desirable to deploy relay nodes in addition to sensor nodes to manage such imbalance. We have also developed a location-wise pre-determined heterogeneous node deployment strategy based on the principle of energy balancing derived from this analysis, leading to an enhancement of network lifetime. Exhaustive simulation is performed primarily to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing and maintaining coverage. The simulation results also show that our scheme does not compromise with other network performance metrics such as end-to-end delay, packet loss, throughput while achieving the design goal. Finally all the results are compared with two competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.