14th International Conference on Software Technologies, Prague, Czech Republic, 26 - 28 July 2019

Abstract To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common data model by making intelligent inferences from low‐level network and device details. Automatic discovery of new devices, monitoring of device state, and invocation of device actions in a generic fashion that is agnostic of network types is enabled. A prototype system called SNoMAC is described that employs the proposed approach operating over UPnP, TR‐069, and heterogeneous sensors. These sensors are integrated by means of a sensor middleware named SIXTH that augments the capabilities of SNoMAC to allow for intelligent management and configuration of a wide variety of sensor devices. A major benefit of this approach is that the addition of new models, protocols, or sensor types merely involves the development of a new network adapter based on an ontology. Additionally, the semantic representation of the network and associated data allows for a variety of client interfaces to facilitate human input to the management and monitoring of the system.

Wireless sensor network (WSN) systems are typically composed of thousands of sensors that are powered by limited energy resources. To extend the networks longevity, clustering techniques have been introduced to enhance energy efficiency. This paper presents a survey on clustering over the last two decades. Existing protocols are analyzed from a quality of service (QoS) perspective including three common objectives, those of energy efficiency, reliable communication and latency awareness. This review reveals that QoS aware clustering demands more attention. Furthermore, there is a need to clarify how to improve quality of user experience (QoE) through clustering. Understanding the users’ requirements is critical in intelligent systems for the purpose of enabling the ability of supporting diverse scenarios. User awareness or user oriented design is one remaining challenging problem in clustering. In additional, this paper discusses the potential challenges of implementing clustering schemes to Internet of Things (IoT) systems in 5G networks. We indicate that clustering techniques enhanced with smart network selection solutions could highly benefit the QoS and QoE in IoT. As the current studies for WSNs are conducted either in homogeneous or low level heterogeneous networks, they are not ideal or even not able to function in highly dynamic IoT systems with a large range of user scenarios. Moreover, when 5G is finally realized, the problem will become more complex than that in traditional simplified WSNs. Several challenges related to applying clustering techniques to IoT in 5G environment are presented and discussed.

It is a common belief that reducing transmission power on a sensor can reduce the energy cost on transmission and therefore reduce the overall power consumption in Wireless Sensor Networks (WSNs). This belief has motivated many studies on transmission power control (TPC). An examination of this belief is presented in this paper, questioning the conditions where TPC can benefit a network. On a single link, we discover there is a limited saving achievable on a sensor itself from reducing transmission power, contradicting previous research. In a star network, however, reducing transmission power can decrease the total energy consumption on other sensors. We conclude that TPC can save energy in a certain network not through reducing a sensor it self's cost as the common belief leads us to expect, but through reducing other sensors' physical and MAC layer receiving.

Data Fusion is the combination of a number of independent search results, relating to the same document collection, into a single result to be presented to the user. A number of probabilistic data fusion models have been shown to be effective in empirical studies. These typically attempt to estimate the probability that particular documents will be relevant, based on training data. However, little attempt has been made to gauge how the accuracy of these estimations affect fusion performance. The focus of this paper is twofold: firstly, that accurate estimation of the probability of relevance results in effective data fusion; and secondly, that an effective approximation of this probability can be made based on less training data that has previously been employed. This is based on the observation that the distribution of relevant documents follows a similar pattern in most high-quality result sets. Curve fitting suggests that this can be modelled by a simple function that is less complex than other models that have been proposed. The use of existing IR evaluation metrics is proposed as a substitution for probability calculations. Mean Average Precision is used to demonstrate the effectiveness of this approach, with evaluation results demonstrating competitive performance when compared with related algorithms with more onerous requirements for training data.

Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results.This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.