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Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

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Smart home systems can provide health care services for people with special needs in their own homes. Briefly defined, such a smart home has special electronics to enable the remote control of automated devices specifically designed for remote health care to ensure the safety of the patient at home and the supervision of their health status. These sensors are linked to a local intelligence unit responsible for analyzing sensor data, detecting emergency situations, and interfacing between the patient at home and a set of people involved in their health care, such as doctors, nurses, emergency services, and paramedics. Smart homes can improve the patient's quality of life and safety through the innovative use of advanced technologies. Telemedicine and telecare are driving forces behind the adoption of smart homes. The telecare medicine information system (TMIS) has drawn worldwide attention for the past 20 years, as modern technologies have made remote delivery of healthcare a reality. TMIS using multidisciplinary research and application involves advanced technologies in information processing, telecommunications, bio-sensing, and artificial intelligence including smart technologies. TMIS leverages the latest mobile and wireless communication technologies and widely available internet infrastructure to deliver quality services to home patients enabling them to remotely access information about their health and obtain telemedical services. TMIS delivers capabilities to remotely provide 24 × 7 health care facilities to patients. Its purpose is to provide patients with convenient and expedited remote health care services, greatly improving the quality and efficiency of health care services. However, the open and insecure nature of the internet poses a number of security threats to patient secrecy and privacy. Security design for TMIS is not trivial. Essential security and privacy are provided by mutual authentication and key agreement protocols. This paper proposes an efficient and secure, bilinear pairing-based, unlink-able, mutual authentication and key agreement protocol for TMIS. The proposed protocol adopts a fuzzy extractor for the identification of patients using the biometric data. The security of the proposed protocol is based on the hardness of the elliptic curve discrete logarithm problem (ECDLP) and elliptic curve computational Diffie-Hellman problem (ECCDHP) to preserve the privacy of the user. The detailed security analysis is discussed, and the results of comparison are provided. INDEX TERMS Smart city, telecare medicine information systems (TMIS), mutual authentication, key agreement protocol, bilinear pairing, fuzzy extractor.