Emerging Sensor-Cloud Technology for Pervasive Services and Applications

With the recent advancement of low-power wireless communication and mobile computing, the wireless sensor network (WSN) paradigm has become an integral part of our daily lives. However, the resource-constrained features of WSNs in terms of memory, computation, energy, communication, mobility, and scalability hinder the large-scale deployment of WSNs. The emergence of cloud computing is seen as an enabling technology to solve many of these constraints. WSN, when integrated with cloud, is considered as a sensor-cloud technology, which can effectively collect, process, store, and analyze real-time data feeds from heterogeneous WSNs and provide quality of service (QoS) provisioning for different community and context-centric sensing applications. The sensor cloud leverages scalable, on-demand, and powerful storage and processing infrastructure of cloud to support complete sensor data lifecycle and provides ubiquitous access of these data to users at lower costs. Despite the huge potential of sensor cloud, it is currently being used at a limited scale in different applications. As this technology is emerging, many research opportunities have just started to unveil. The objective of this special issue was to explore the potential of this technology and different perspectives of materializing the vision of sensor-cloud.

After a rigorous and long review process, among a good number of submissions, we finally accepted only 8 papers for this special issue. The next section briefly discusses the contributions from different angles.

Main Themes Presented in the Accepted Papers. M. A. Hossain in his paper entitled “Framework for a cloud-based multimedia surveillance system” describes different design issues related to a cloud-based multimedia surveillance system. The paper talks about cloud deployment architecture, media acquisition strategy, cloud storage, media processing, resource allocation, notification and sharing, big data analytics, security and privacy, and cloud-based system performance. Based on these design issues, the paper proposes a cloud-based multimedia surveillance framework. The author also reports on the development of a prototype system. To show the efficiency of the approach, some results related to dynamic workload, cost trade-off, and average task waiting time are also presented.

S. S. Ara et al.'s work entitled “Web-of-objects based user-centric semantic service composition methodology in the internet of things” presents a user-demand based service composition by showing a use case of Web-of-Objects (WoO). The authors note that general objective of the Web-of-Objects (WoO) is to simplify object and application deployment, maintenance, and operation of IoT (internet of things) infrastructures. WoO also aims to provide user-centric IoT service by enabling object virtualization and semantic ontology-based service composition. The authors describe in this work a semantic functional module for user centric service composition in WoO platform. In the presented approach, ontologies describe the relation among objects, services, and rules to compose new services dynamically.

A review of cyber-physical system (CPS) in healthcare technologies is presented by S. A. Haque et al. in their paper “Review of cyber-physical system in healthcare”. This review could be useful for the researchers as it gives a general overview of CPS, makes a classification of CPS in healthcare, and discusses the challenges and issues in CPS. An interesting part of this paper would be the comparisons among different previous works based on three major aspects: Application, Architecture, and Sensing.

S. Chandrasekaran et al.'s work “Sensor grid middleware metamodeling and analysis” proposes a sensor-grid architecture. The proposed sensor grid architecture is basically based on open geospatial consortium (OGC) protocol whichcomprises of a data computing grid, a sensor-web enablement, a sensor-grid middleware, sensor nodes, and end users. The end users can access information about sensor processes through the SWE (sensor web enablement) standards. The SWE component includes encoding and web service standards defined by OGC.

M. K. Saini et al.'s paper entitled “From smart camera to SmartHub: embracing cloud for video surveillance” is an interesting work that investigates the reason behind the scarce commercial usage of smart cameras. The main contributions of this work are that the authors provide a comparative assessment of smart cameras with the conclusion that smart cameras are an inefficient choice for growing multicamera applications, and they propose the cloud entity SmartHub that overcomes the limitations of smart cameras and a framework to make design decisions. Authors note that the given framework can be used to calculate an adequate number of cameras to be connected to a SmartHub for a given camera placement scenario.

“Cloud-based collaborative media service framework for HealthCare” by M. S. Hossain and G. Muhammad is another paper on healthcare issues that uses cloud-based collaborative media service. The proposed framework in this work uses collaborative service for voice pathology detection, where doctors, caregivers, and patients collaborate with each other through emerging multimedia communication technologies such as video-conferencing, web conferencing, voice mail, and discussion board. During the session, collaborative users (caregivers and patients) need to use different handheld devices equipped with a rich set of sensors such as camera, audio, Wi-Fi/3G/4G radios, accelerometer, and microphone to access the media (image, video, and audio/voice) content ubiquitously.

Middleware is formally defined as the computer software that provides services to software applications beyond those available from the OS (operating system). As it can be termed as the “software glue,” T. H. Kim et al. in their contribution “A middleware architecture for dynamic reconfiguration of agent collaboration spaces in indoor location-aware applications” propose DRAS (dynamic reconfigurable agent space), which is a special middleware architecture based on service agents. The authors use DRAS middleware for the indoor location-aware applications that could provide interactive capability with the surrounding physical environment. The main idea of the work is that agents can be distributed in the form of Erlang processes in the stationary nodes and can provide real-time service responses by the distribution of network traffic and service processing. Erlang is basically a programming language that supports fast process creation, control of large numbers of processes, and fast communication among processes for distributed systems. In DRAS, an agent is implemented as an Erlang process as this is light-weight (grow and shrink dynamically) with small memory footprint, fast to create and terminate, and the scheduling overhead is low.

Finally, A. M. J. Sarkar's paper “Hidden Markov mined activity model for human activity recognition” considers an environment in which a set of objects (e.g., light, door, and faucet) are embedded with sensors. A sensor is attached to an object in a way such that it is possible to determine the state of the object when used. Given a set of activities to monitor and object names (with embedded sensors), the purpose of the activity recognition system is to recognize the current activity of a person depending on the sequence of objects used at a given time. Based on such scenario, the author proposes a novel HMM (hidden Markov model) based activity model for human activity recognition in a home setting, in which the transition to the next state at a given time depends on the current state and the observation sequence.