For conserving energy, duty cycle is defined by setting up the active and sleep periods of network nodes. In beacon enabled networks, to provide support for duty cycle, the IEEE 802.15.4 standard uses optional super-frame structure. This duty cycle is usually fixed and does not consider the topology changes that often occur in dynamic sensor networks. In this paper, existing energy conserving duty cycling approaches for 802.15.4 networks especially the adaptive duty cycling techniques for wireless sensor networks are summed up. Also, this paper highlights the shortcomings of the proposals in the literature, such as induced additional latency, so that they may not support the practical scenarios of Internet of Things (IoT). Further, this study highlights a gross shortcoming that relative performance comparison of RL-based proposals cannot be performed without using a benchmarking framework and real test-bed environment. In this paper, we have presented the future research directions that would lay the foundation for successful development of energy efficient RL-based duty-cycling techniques.

Since last few years, computers have become a prominent part of the court of law. Courts generate an enormous amount of unstructured text on daily basis. Extraction of the desired information from this unstructured legal text is one of the major issues. So, there is a need to develop an intelligent system that can automatically find useful and critical information from available text. Such system will help judges and lawyers in their judgments and case preparations, common people in understanding law and finding appropriate lawyer for their legal issues. Therefore, in this research, Punjab University Legal Mining System (PULMS) is developed using three different supervised machine learning algorithms; Conditional Random Field (CRF), Maximum Entropy (MaxEnt) and Trigrams N Tag (TNT). To train the system, 304 criminal miscellaneous judgments of the Lahore High Court (LHC) of Pakistan are manually tagged for nine Named Entities (NE). After training, among three machine learning algorithms the system achieved significant precision, recall, and f-measure using CRF which are 0.97, 0.87, and 0.89 respectively.

Central research questions addressed within Criminology are how the geographical displacement of crime can be understood, explained, and predicted. The process of crime displacement is usually explained by referring to the interaction of three types