Stochastic Performance Trade-offs in the Design of Real-Time Wireless Sensor Networks

Future sensing applications call for a thorough evaluation of network performance trade-offs so that desired guarantees can be provided for the realization of real-time wireless sensor networks (WSNs). Recent studies provide insight into the performance metrics in terms of first-order statistics, e.g., the expected delay. However, WSNs are characterized by the stochastic nature of the wireless channel and the queuing processes, which result in non-deterministic delay, throughput, and network lifetime. For the design of WSNs with predictable performance, probabilistic analysis of these performance metrics and their intrinsic trade-offs is essential. Moreover, providing stochastic guarantees is crucial since each deployment may result in a different realization. In this paper, the trade-offs between delay, throughput, and lifetime are quantified through a stochastic network design approach. To this end, two novel probabilistic network design measures, quantile and quantile interval, are defined to capture the dependability and predictability of the performance metrics, respectively. Extensive evaluations are conducted to explore the performance trade-offs in real-time WSNs

A Social Force Model for Adjusting Sensing Ranges in Multiple Sensing Agent Systems

In previous work of multiple sensing agent systems (MSASs), they mainly adjust the sensing ranges of agents by centralized heuristics; and the whole adjustment process is controlled in centralized manner. However, such method may not fit for the characteristics of MSASs where the agents are distributed and decide their activities autonomously. To solve such problem, this paper introduces the social force model for adjusting the sensing ranges of multiple sensing agents, which can make the agents adjust their sensing ranges autonomously according to their social forces to other agents and the sensing objects. Based on the social force model, the coverage and optimization models are presented for both point-type and area-type objects. The presented model can produce appropriate social forces among the sensing agents and objects in MSASs; thereby the system observability and lifetime can be improved

A Nodes Deployment Algorithm in Wireless Sensor Network Based on Distribution

Abstract: Wireless sensor network coverage is a basic problem of wireless sensor network. In this paper, we propose a wireless sensor network node deployment algorithm base on distribution in order to form an efficient wireless sensor network. The iteratively greedy algorithm is used in this paper to choose priority nodes into active until the entire network is covered by wireless sensor nodes, the whole network to multiply connected. The simulation results show that the distributed wireless sensor network node deployment algorithm can form a multiply connected wireless sensor network

Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption

Coverage strategy for heterogeneous nodes in wireless sensor network based on temporal variability of farmland

Ako se razmotre različiti vremenski uvjeti na poljoprivrednom zemljištu, stopa pokrivenosti bežične mreže senzora (WSN - Wireless Sensor Network) obično je mala. Mogu se pojaviti problemi slijepih točaka i prenatrpanosti vrućim točkama. Predlažemo strategiju pokrivenosti za heterogene čvorove u WSN na temelju vremenskih promjena koje utječu na obradivost, a koja predviđa ključne čvorove primjenom modela predviđanja ključnog čvora u skladu s vremenskim promjenama na imanju. Uvođenjem obnovljivih čvorova energije, mogu se odrediti položaji heterogenih čvorova u mreži. Zadatak se ponovo prebacuje na heterogene čvorove ovisno o zaostaloj energiji čvorova te se stanje čvorova u mreži dinamički podešava. Simulacija pokazuje da CSHN može učinkovito smanjiti stopu propadanja čvora i potrošnju energije mreže i tako produžiti opstojnost mreže te ujednačiti pokrivenost i potrošnju energije mreže.Given temporal variability of farmland, the coverage rate of wireless sensor network (WSN) is usually low. There may arise the problems of blind spot area and congestion of hot spots. We propose a coverage strategy for heterogeneous nodes in WSN based on temporal variability of farmland, which predicts the key nodes using key node prediction model according to temporal variability of farmland environment. Through introduction of renewable energy nodes, the positions of heterogeneous nodes in the network can be determined. The task is re-allocated to the heterogeneous nodes depending on the residual energy of nodes, and the state of nodes in the network is adjusted dynamically. Simulation shows that CSHN can effectively reduce the node death rate and network energy consumption, while prolonging the survival of network and equalizing coverage and network energy consumption

Design Model and Deployment Fashion of Wireless Sensor Networks

The ease deployment of Wireless sensor networks (WSNs) in the harsh and hard environment possesses a paved because the way it is. They are formed by sensor nodes which are responsible for examining environmental and corporal conditions to perform data processing. In this chapter, the manner of deployment will be presented, and how they communicate over a wireless link to unite the necessities of a specific application will be shown

A framework of optimizing the deployment of IoT for precision agriculture industry

The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer’s practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer’s experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment