Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning-(ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold-based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold-based classification from existing works and finds the need for an efficient ML-based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML-based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML-based techniques for future research. This study finds that present ML-based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML-based techniques are far superior to threshold-based techniques.The study concludes that Self-Adaptive-based FDS, as

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Mobile Ad hoc networks (MANETs) are self organized multi-hop networks, without any infrastructure such as base stations or access points. Due to the mobility and absence of any central administration, the resources of MANETs are limited. If there is any congestion in the network, it puts a great strain on the already scarce resources and severely affects the performance of such networks. Multi path routing is considered as advantageous over single path routing, due to the many benefits it offers. However, these benefits do not come without their associated costs. In this paper, we propose a general metric to define scalability of a routing method. We further propose and implement a new load-balancing routing protocol, which retains the benefits of multiple paths, while at the same time keeping the overheads of routing, as close to single path routing, as possible. The proposed scheme dynamically distributes traffic through different available paths, so that no single path is flooded. Priority is assigned to available paths and paths with higher priority (better routes) are used more often than those with lower priorities. To keep our method light-weight and scalable, we control the Degree of Distribution (DoD) value (number of alternate paths used), to reap maximum benefits at minimum cost. To further reduce the overheads and decrease access time, optimized insertion and path selection are provided. An index to the RouteList table has been added, which reduces the access and insertion time to O(m ? n d ) and O(m) respectively, which is within a constant difference of Single path routing methods. Simulation results demonstrate that the proposed solution shows significant improvements in network metrics such as packet loss ratio, end to end delay, throughput and packet delivery, without any increase in routing overheads. Results also verify that this model is very efficient and scalable.

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