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Pervasive and mobile computing promises to simplify life via digital platforms and devices that sense, adapt and respond to various needs [1]. In other words, pervasive and mobile computing has the goal of creating miniaturized and distributed computing nodes that work jointly to process information senses from various locations. For that reason, modern devices have microprocessor and sensors, used to collect and transmit data for analysis, more powerful and normally centralized systems are responsible for validation and processing. This paper explores pervasive and mobile computing and deals briefly with sensor networks, home systems and electronic health systems. Evidently, pervasive and mobile computing improves data collection and response to various situations in remote and tactical environments [1,2].

The field of pervasive computing is continuing to evolve as computer processors become smaller and more powerful, allowing manufactures of sensor networks, home systems and electronic healthcare systems to embed miniaturized processors in these systems while improving power consumption and mobility. As more people engage in cyberspace, there is a corresponding growth in the application of mobile technology, which is computing technology on the go, using devices such as tablets, wearable sensors, portable computers and smartphones.

Pervasive and mobile computing enables users to respond flexibly to various needs. This research gives an overview of the current state of pervasive and mobile computing, focusing on wireless sensor networks. To achieve this, the paper is segmented into two sections. The first section gives a broad perspective on distributed pervasive system. The second section focused on pervasive computing in healthcare. The conclusion focuses on the future trends in electronic healthcare systems.

Distributed Pervasive Systems

Mobile-based systems have become critical strategic tools in current competitive business environment [2]. With advancement in technology, the rate of change in the business world is unprecedented. This has resulted in business transformation. To ensure that the adoption of mobile computing realizes value to an individual or business entity, a top down approach and lean approaches are utilized. Pervasive and mobile computing systems have become the information pillars, and the rapidly advancing technologies highlight the limitations in flexibility of conventional systems. Distributed pervasive systems are the next generation of distributed systems marked by small notes and mobility [1]. Typically, distributed pervasive systems are embedded in large systems. One key aspect of these systems is contextual change. Distributed pervasive systems are a part of the environment in which changes are accounted. For example, in the healthcare environment, distributed systems are embedded in medical devices to monitor patients’ health. In distributed pervasive systems, resource sharing is mandatory. Nodes provide sharable information and services, while maintaining simplicity. Ad hoc composition implies that different users can use each node in various ways. Examples of pervasive systems include electronic health systems, home systems and sensor networks. Home systems are considered to be self-governing, and, thus, do not require system administrator and provide space for each user. For example, an automated water sprinkler contains a motherboard and a programmable logic, which makes it self-organizing. Other home systems built around a computer include printers and laptops. Evidently, pervasive and mobile computing home systems are evolving to include personal digital assistants (PDAs) and smart TVs. Grid-tied solar PV systems are able to inject excessive power to the grid by intelligently managing the power generated and the energy consumed.

Electronic health systems are medical devices that work jointly to monitor patients’ health condition. For example, an electronic health system may include motion sensor, temperature sensor and heart pulse sensor of a patient. All the units collect the data and transmit it to a centralized processing unit. As of this writing, doctors can remotely monitor the vitals of their patients using wearable computers and sensors [3]. These medical devices are also equipped with data transmission infrastructures connected to a centralized electronic health record system. In that regard, the electronic health systems improve service delivery and lessen the pressure on healthcare personnel, particularly the need to be near patients around the clock to monitor their vitals. At the core of these pervasive distributed systems is the need for security, fast communication and feedback channel. It follows that success largely depends on the efficiency of communication link between the device and the actual healthcare personnel. Similar to any other technology, security concerns are linked to pervasive distributed systems. For that reason, there is a growing debate and research regarding improving privacy and safety of the patients’ electronic healthcare systems.

The other example of pervasive and mobile computing application is found in wireless sensor networks. Typically, sensor networks are low powered devices used to sense specific changes and transmit the information through wireless communication. For example, sensor networks can be installed in a service provider’s base stations (BTS) to sense power outages and communicate the information to the command centre. Normally, each sensor receives, processes, and stores data before sending it to the command centre. The following section in detail analyses sensor networks.

Distributed Pervasive Systems and Wireless Sensor Networks

IT-based organizations should have the ability to adapt to technological changes taking place in their internal or external environment. The adaptation entails implementing technologies that have the capacity to increase their competitive advantages. Pervasive and mobile computing is one of the technological concepts that has the capacity to transform an entity or individual by involving wireless communication. Wireless communication has undergone an outstanding technological evolution [4]. Designers and manufacturers are progressively departing from the conventional communication through a single point-to-point basis within a central controlling base transceiver station (BTS) to the multi-user MIMO networks. Multi-User MIMO networks offer more benefits than traditional point-to-point MIMO sensor networks. For example, MU-MIMO works with cheap antenna terminals. In addition, MU-MIMO involves a simplified resource allocation scheme because all the active terminals use bins and frequency. However, MU-MIMO is limited in terms of scalability. As a consequence, advances in distributed pervasive computing and mobility has led to large-scale MIMO networks, which counter this limitation by using more service antennas in transmission of queried or transferred data. In addition, channel fading is a serious cause of degraded signal at the receiver, ending wireless communication. Furthermore, in distributed pervasive computing, more sensor networks can be used to mitigate fading and improve the quality of the transmitted signal.

Distributed MIMO

This modern mode of wireless communication is motivated by its capacity to meet the demands of next generation communications. Distributed MIMO (multiple sensor networks) outperform point-to-point MIMOs, due to the improved capacity and quality of service [5]. Distributed Multiple-Input-Multiple-Output (MIMO) or simply Distributed MIMO is a wireless communication technology that uses multiple transceivers to transfer more data simultaneously [2].

Large Scale D-MIMO

In contrast to Distributed MIMO, large-scale MIMO (massive sensor networks) technology is used in wireless communication links to provide signal enhancement as well as additional data capacity [6]. Large Scale D-MIMO is wireless communications system that employs many relays with multiple antennas. The configuration is characterised by multiple sensors (antennas) for each relay. More specifically, an array of antennas (network sensors) serve many terminals simultaneously within the same time-frequency resource.

The motivation behind this configuration is to reap the benefits of traditional MIMO systems on a large scale [7]. The large-scale MIMO communication systems are gaining popularity due to high achievable data transmission rates and improved reliability for next-generation wireless networks [7]. The manifold signal path generated by multiple transmission and reception sensors translates into a large throughput. In essence using an array of sensor networks reduces the overall amount of power consumed.

The Prospective of Large Scale Sensor Networks

One of the benefits of large-scale sensor networks is the ability to increase capacity simultaneously with the radiated energy efficiency [8]. The increase in capacity is attributed to aggressive spatial multiplexing. On the contrary, energy efficiency is improved due to the presence of the large number of antennas, which enable energy to be focused into regions in space. Additionally, these networks can be built with low power or low-cost low-power components. Large-scale sensor networks support the development of next generation broadband networks, which will be energy efficient, robust, secure and effective in spectrum utilisation. Additionally, distributed pervasive sensor networks are projected to serve as the backbone of the wireless connection between the cloud computing and the Internet of Things (IoT). Furthermore, sensor networks will improve increases robustness against accidental fabricated interferences and intentional signal jamming [8].

Challenges Facing Distributed Pervasive Sensor Networks

One of challenges sensor networks faces is channel reciprocity [8]. Ideally, the hardware chains in large-scale sensor network should be reciprocal between the uplink and the downlink. However, wireless communication channel reciprocity exists because of communication system calibration-based problems. In other words, the array of hardware can only transmit coherent signals if they are well calibrated. The existing incongruity within the receiver equipment is addressed through transmitting a coherent beam. In addition, one sensor can serve as reference sensor, while the other sensors are designed to derive the compensation factor for that reference sensor.

Conclusion

This paper provided an overview of distributed pervasive systems, with an emphasis on wireless sensor networks. From the discussion, it is evident that advances in computing technology enables an array of pervasive computing applications, including smart homes, electronic healthcare systems and wireless sensor networks.

Over time, ISPs will identify the challenges linked to the implementation of current innovative sensor networks, especially when there is a need to customize the sensor networks. This is likely to push designers and vendors to develop new wireless communication technologies by improving and optimizing the existing conventional communication technologies. One of the projected benefits of technological advancement in sensor networks is the increase data flow rate and the reduction in operational costs, due to low energy consumption.

Further, there will be a significant growth in the automation of most business and technical processes, increasing network performance at low cost and minimal errors. With central data repository, ISPs avoid extra work and maintenance cost incurring in remote BTS. Additionally, companies have a better control of their operations. However, there are some barriers to adoption of distributed pervasive sensor based systems.

Challenges may emerge from an ISP’s environment including internal and external forces, technology push and market pull. At the centre of these challenges are integration and security barriers, especially across cloud layers and mobile platforms. In addition, there is inadequate talent for the emerging pervasive and mobile computing technology. Furthermore, budget constraints are also key barriers to adoption of distributed pervasive systems. In response to these challenges, there is a need for further research and development in the realm of pervasive and mobile computing.