Real-Time QoS Routing Protocols in Wireless Multimedia Sensor Networks: Study and Analysis

Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.https://doi.org/10.3390/s15092220

Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime

Energy-efficient Compressive Data Gathering Utilizing Virtual Multi-input Multi-output

Data gathering is an attractive operation for obtaining information in wireless sensor networks (WSNs). But one of important challenges is to minimize energy consumption of networks. In this paper, an integration of distributed compressive sensing (CS) and virtual multi-input multi-output (vMIMO) in WSNs is proposed to significantly decrease the data gathering cost. The scheme first constructs a distributed data compression model based on low density parity check-like (LDPC-like) codes. Then a cluster-based dynamic virtual MIMO transmission protocol is proposed. The number of clusters, number of cooperative nodes and the constellation size are determined by a new established optimization model under the restrictions of compression model. Finally, simulation results show that the scheme can reduce the data gathering cost and prolong the sensor network’s lifetime in a reliable guarantee of sensory data recovery quality

NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application