Lifetime improvement method using mobile sink for IoT service

Abstract

In this paper, we describe a lifetime improvement method using mobile sink node for Internet of Things (IoT) device and discuss the major technologies involved in IoT. Additionally, we discuss the potential scope of IoT and the major technologies involved in IoT infrastructure. Conventional network was based on static sink which is unsuitable for resource constrained Wireless Sensor Networks (WSNs). WSNs can be large scale networks and it is impossible to perform as normal without connectivity. In IoT, physical objects home appliances, temperature sensor, smart phone, table PC etc. will be context aware to sense the environment around them and interact with each other. The devices in IoT are powered by a battery with limited power, which is dissipated during sensing, processing and data transmission/reception. For IoT, devices are required to be made sustainability by embedding connectivity and intelligent into them using technologies such as WSNs. Therefore connectivity and energy efficiency are the key issues in WSNs. We propose a simple method using mobile sink based on Travelling Salesman Problem (TSP). The proposed method uses less memory and less overhead cost than other schemes including relay node, and is therefore appropriate for implementation in WSNs. We will prove that the method can guarantee network connectivity. The performance of our method is demonstrated by simulation results. Not only simulation result shows that the proposed method can extend the network lifetime and connectivity, but also reduces unnecessary relay node's energy consumption by using mobile sink.

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

“…As such, any efficient solution to the TSP can be applied to solve many real world problems, such as transportation control [4], network management [5], and scheduling [6]. Assuming that d ( c i , c j ) represents the distance between each pair of cities c i and c j , the TSP asks for a solution—that is, a permutation 〈 c π (1) , c π (2) ,…, c π ( n ) 〉 of the given n cities—that minimizes $\begin{array}{c}D=\left(\underset{i=\mathrm{normal1}}{\overset{n-\mathrm{normal1}}{{\displaystyle false\sum }}}d\left(c_{\pi \left(i\right)},c_{\pi \left(i+\mathrm{normal1}\right)}\right)\right)+d\left(c_{\pi \left(n\right)},c_{\pi \left(\mathrm{normal1}\right)}\right).\end{array}$ In short, (1) gives the distance D of the tour that starts at city c π (1) , visits each city in sequence, and then returns directly to c π (1) from the last city c π ( n ) .…”

“…In the area of combinatorial optimization research [ 1 ], the traveling salesman problem (TSP) [ 2 ] has been widely used as a yardstick by which the performance of a new algorithm is evaluated, for TSP is NP-complete [ 3 ]. As such, any efficient solution to the TSP can be applied to solve many real world problems, such as transportation control [ 4 ], network management [ 5 ], and scheduling [ 6 ]. Assuming that d ( c i , c j ) represents the distance between each pair of cities c i and c j , the TSP asks for a solution—that is, a permutation 〈 c π (1) , c π (2) ,…, c π ( n ) 〉 of the given n cities—that minimizes In short, ( 1 ) gives the distance D of the tour that starts at city c π (1) , visits each city in sequence, and then returns directly to c π (1) from the last city c π ( n ) .…”