High power to guarantee strict performance requirement and low power to avoid energy depletion result in an inevitable conflict for a renewable energy harvesting communication system with finite energy storage. This paper proposes a generic approach to study the per-flow performance in such a multichannel system multiplexed by multiple flows. The queueing delay constraint and energy storage constraint are constructed to express the probabilistic bound of queuing delay and that of energy depletion, respectively. We study these constraints with the statistical information of the processes, including traffic arrivals and service, energy harvesting, and consumption. The lower bound of the long-term maximum per-flow throughput is then derived to meet the constraints under a specific system. The accuracy of the proposed approach is validated by simulation experiments. The analysis reveals how the sustained throughput is affected by various factors, such as the queueing delay and energy storage constraints, the packet size, the energy block size, the traffic scheduling schemes, the bandwidth allocation schemes as well as the interdependence among the channel service processes. Particularly, the analysis also provides valuable insight into traffic admission control from the viewpoint of small queueing delay and finite energy storage.

UN Department of Economics and Social Affairs predicted that the world population will increase by 2 billion in 2050 with over 50% from the Sub-Saharan Africa (SSA). Considering the level of poverty and food insecurity in the region, there is an urgent need for a sustainable increase in agricultural produce. However, farming approach in the region is primarily traditional. Traditional farming is characterized by high labor costs, low production, and under/oversupply of farm inputs. All these factors make farming unappealing to many. The use of digital technologies such as broadband, Internet of Things (IoT), Cloud computing, and Big Data Analytics promise improved returns on agricultural investments and could make farming appealing even to the youth. However, initial cost of smart farming could be high. Therefore, development of a dedicated IoT cloud-based platform is imperative. Then farmers could subscribe and have their farms managed on the platform. It should be noted that majority of farmers in SSA are smallholders who are poor, uneducated, and live in rural areas but produce about 80% of the food. They majorly use 2G phones, which are not internet enabled. These peculiarities must be factored into the design of any functional IoT platform that would serve this group. This paper presents the development of such a platform, which was tested with smart irrigation of maize crops in a testbed. Besides the convenience provided by the smart system, it recorded irrigation water saving of over 36% compared to the control method which demonstrates how irrigation is done traditionally.

There are several IoT platforms providing a variety of services for different applications. Finding the optimal fit between application and platform is challenging since it is hard to evaluate the effects of minor platform changes. Several websites offer reviews based on user ratings to guide potential users in their selection. Unfortunately, review data are subjective and sometimes conflictingindicating that they are not objective enough for a fair judgment. Scientific papers are known to be the reliable sources of authentic information based on evidence-based research. However, literature revealed that though a lot of work has been done on theoretical comparative analysis of IoT platforms based on their features, functions, architectures, security, communication protocols, analytics, scalability, etc., empirical studies based on measurable metrics such as response time, throughput, and technical efficiency, that objectively characterize user experience seem to be lacking. In an attempt to fill this gap, this study used web analytic tools to gather data on the performance of some selected IoT cloud platforms. Descriptive and inferential statistical models were used to analyze the gathered data to provide a technical ground for the performance evaluation of the selected IoT platforms. Results showed that the platforms performed differently in the key performance metrics (KPM) used. No platform emerged best in all the KPMs. Users' choice will therefore be based on metrics that are most relevant to their applications. It is believed that this work will provide companies and other users with quantitative evidence to corroborate social media data and thereby give a better insight into the performance of IoT platforms. It will also help vendors to improve on their quality of service (QoS).

Energy evaluation of wireless transmission for data service under different QoS (quality of service) constraints is a hot topic in green communications. In this paper, we employ the theory of the stochastic network calculus to investigate the relationship between the traffic arrival rate and energy harvesting rate under the delay and energy storage constraints. We first construct a wireless system model which works only by consuming the harvested renewable energy. Also, stochastic traffic arrivals, packet size as well as a two-state Markov chain interference on energy harvesting are jointly considered to ensure the accuracy of the analysis. We derive the minimum energy harvesting rate needed to satisfy the delay and energy storage constraints under a given average arrival rate. The efficiency of the harvested energy is also studied and shown to be convex in the numerical simulation. Numerical results illustrate that while the minimum energy harvesting rate is positively correlated with the packet size, it is negatively related to the delay requirement, under a given arrival rate. Additionally, the probabilistic bound of energy insufficiency is shown to be positively correlated with the transition cycle of the interference.