The sensor web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, sensor web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any sensor web should provide support for quality of observation (QoO) requirements. In practise, however, only few sensor webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner. This survey aims to study why and how observation quality should be addressed in sensor webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within sensor webs. Second, it proposes a QoO-oriented review of 29 sensor web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware sensor web solutions.

This study presents an oversampling-based low-latency, high-resolution and low-cost timing synchronisation technique for digital receivers. Traditional timing synchronisation employs matched filter to estimate the signal coarse time of arrival. Here, latency increases through oversampling and resolution improves via higher bandwidth. The proposed method uses single bit quantisation to employ XNOR blocks instead of multiplier and accumulator blocks in the traditional method. This substantially decreases complexity incorporating less field-programmable gate array (FPGA) surface area.

Sensors are gaining a ubiquitous status over many application domains with regard to enabling data-driven decision making and smart functionality. Integration of sensors with cloud-based data storage and analysis has the potential to significantly enhance the efficiencies, resilience and adaptability of managing smart infrastructure management. In this context, a standout application is smart grid, which provides an electricity delivery service with the ability to monitor, protect and optimize various operations of its connected elements from service provider to consumer. An ability to read and manage smart grid measurements remotely using wireless sensors is an important advantage that allows the grid operators to balance loads effectively and enable on-demand services for various entities. However, the adoption of cloud infrastructure to manage sensor data in a smart grid poses significant risks to data security and consumers privacy. The data maybe exposed to malicious or unwarranted parties, with the potential for various security attacks that may impact data integrity, availability and accountability. We propose a secure and practical billing model using homomorphic encryption within a cloud-based data processing framework. Moving billing management into the cloud securely, with on-demand data retrieval and statistical computations is a major strength of the proposed framework.

The authors have addressed the issue of energy saving in packet retransmission by reducing the packet loss in the first attempt. They have formulated the problem as Markov decision process and mapped the system into various states based on system dynamics like channel gain, buffer space, battery energy and harvested energy. In wireless sensor network, adaptive policy management is the best approach to work out with the variation of system states in a dynamically changing environment. The policies are defined regarding various controllable parameters like transmission power, modulation scheme, and transmission rate. The solution is sought regarding best policy selection as per the individual state of the system. They have evaluated the performance of the proposed optimum transreceiver scheme by extensive simulations with existing schemes like optimum energy allocation, adaptive modulation and adaptive power management for the various performance metrics like net bit rate and energy consumption in retransmission of packets for different system states.

Coverage problem in sensor network ensures quality of monitoring of a given region. Depending on applications, different measures of coverage are there such as area coverage, sweep coverage, barrier coverage and so on. Barrier coverage ensures monitoring only the boundary of a region. Barrier coverage is a critical issue in wireless sensor networks for border protection, the performance of which is highly related to the locations of sensor nodes. The objective of the sensors is to detect objects that cross the boundary. The primary objective of this study is to restore barrier coverage after sensor failure with the help of mobile sensors. Battery life of the sensors is one of the critical parameters of a sensor network. To increase the lifetime of the sensor network, the authors’ restoring scheme tries to keep the total displacement incurred by the mobile sensors to minimum. A localised distributed barrier coverage restoring scheme is proposed. It first finds the existence of alternate barrier locally. If alternate barrier is not found, an alternate barrier is reconstructed by shifting the existing nearby sensors in a cascaded manner. Simulation results are presented in detail to evaluate the performance of their algorithm.