Network accessibility has become increasingly important in today's fast-paced world. In fact, it is difficult to imagine our new generation that cannot benefit from a wireless network. Companies across a broad spectrum, manufacturers, warehouses, retailers, schools, hospitals, hotels and service facilities are implementing wireless networks in record numbers. The benefits they find include mobility, simpler installation and lower cost of ownership. The research on wireless technologies is of key importance and therefore, a lot of the researchers in electrical and computer engineering schools intend to contribute to this field. Similarly, we at WINS also focus on multiple areas of wireless communications and most of them are/were funded and encouraged as follows: 

Current Projects:
Title: Towards Smart and Connected Healthcare via Named Data Networking
Lead and Sole PI: Syed Hassan Ahmed
Sponsor: Allen E. Paulson College of Engineering and Computing (Faculty Research Seed Grant)
Duration: 2018-2019.

Abstract: During the past decades, the Internet of Things (IoT) enabled healthcare sector has significantly advanced to improve the quality of healthcare services to the patients under observation or treatment in hospitals as well as homes. As a result, we have seen smart and connected medical devices being manufactured and utilized in many ways. The ultimate goal is to provide ease of access to the patients' medical data (e.g., medical health records). This access is made available to different entities including doctors, caring staff, patients, and family members of the patient. IoT could automate healthcare services and provides remote monitoring of the patient from anywhere. Nevertheless, the current IP-based communication makes connected medical devices prone to security breaches, limited mobility support, privacy concerns, and data loss. Named Data Networking (NDN), a future Internet architecture, provides robust data retrieval as it does not require to map/keep track of IP addresses and supports seamless mobility with ``Named Data''. Security is inherited in NDN as every data packet is duly signed before being forwarded to the consumer. In this article, we explore the potentials of applying NDN to the smart healthcare sector and, for the very first time, we propose a generic NDN-Care architecture for smart hospitals that support named data communications at various levels. Also, we outline research challenges to be considered in ongoing research on NDN-Care.

Title: Cloud-Based Collaborative Demand Response for Manufacturing in the Smart Grid
Lead PI: Fadwa Dababneh (Department of Manufacturing Engineering, Georgia Southern University, Statesboro)
Co-PI: Syed Hassan Ahmed
Sponsor: Allen E. Paulson College of Engineering and Computing (Faculty Research Seed Grant)
Duration: 2018-2019.

Abstract: The Smart Grid opens many opportunities for electricity suppliers and customers to maintain grid stability and reduce electricity cost. In particular, automated demand response programs have gained much attention in both industry and academia. These programs are can realize the functionality of the Smart Grid by monitoring and controlling building loads such as HVAC, lighting, driers, etc. Unfortunately, such loads are geared toward commercial and residential electricity users and cannot be extended to manufacturers. This is due to complex production dynamics and decision-making challenges hindering the quantification and control of manufacturers’ energy loads. Moreover, computational and communication capabilities, needed to control manufacturing loads in response to Smart Grid notifications, are also impeded by the severity of risks associated with erroneous data communication and the corresponding cumbersome information flow requirements. Hence, cloud-based collaborative demand response bridging Smart Grid and manufacturing communication and decision-making activities is proposed. The proposed architecture will provide dual verification such that data and information can be verified and carried at the local memory and cloud. The proposed architecture will entail robust communication interfaces and real-time decision- making algorithms needed to realize the benefits of demand response from the manufacturing sector.